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A Comparison of Different Methods for Evaluating Diet, Physical Activity, and Long-Term Weight Gain in 3 Prospective Cohort Studies.

Abstract

The insidious pace of long-term weight gain (∼ 1 lb/y or 0.45 kg/y) makes it difficult to study in trials; long-term prospective cohorts provide crucial evidence on its key contributors. Most previous studies have evaluated how prevalent lifestyle habits relate to future weight gain rather than to lifestyle changes, which may be more temporally and physiologically relevant. Our objective was to evaluate and compare different methodological approaches for investigating diet, physical activity (PA), and long-term weight gain. In 3 prospective cohorts (total n = 117,992), we assessed how lifestyle relates to long-term weight change (up to 24 y of follow-up) in 4-y periods by comparing 3 analytic approaches: 1) prevalent diet and PA and 4-y weight change (prevalent analysis); 2) 4-y changes in diet and PA with a 4-y weight change (change analysis); and 3) 4-y change in diet and PA with weight change in the subsequent 4 y (lagged-change analysis). We compared these approaches and evaluated the consistency across cohorts, magnitudes of associations, and biological plausibility of findings. Across the 3 methods, consistent, robust, and biologically plausible associations were seen only for the change analysis. Results for prevalent or lagged-change analyses were less consistent across cohorts, smaller in magnitude, and biologically implausible. For example, for each serving of a sugar-sweetened beverage, the observed weight gain was 0.01 lb (95% CI: -0.08, 0.10) [0.005 kg (95% CI: -0.04, 0.05)] based on prevalent analysis; 0.99 lb (95% CI: 0.83, 1.16) [0.45 kg (95% CI: 0.38, 0.53)] based on change analysis; and 0.05 lb (95% CI: -0.10, 0.21) [0.02 kg (95% CI: -0.05, 0.10)] based on lagged-change analysis. Findings were similar for other foods and PA. Robust, consistent, and biologically plausible relations between lifestyle and long-term weight gain are seen when evaluating lifestyle changes and weight changes in discrete periods rather than in prevalent lifestyle or lagged changes. These findings inform the optimal methods for evaluating lifestyle and long-term weight gain and the potential for bias when other methods are used.